By Anil Skariah:
The concept of Robust Product Design, making the product or process insensitive to variation, has many advantages despite some disadvantages
Robust product design is a concept from the teachings of Dr. Genichi Taguchi, a Japanese quality Guru. It is defined as reducing variation in a product without eliminating the causes of the variation. In other words, making the product or process insensitive to variation. This variation (sometimes called noise) can come from a variety of factors and can be classified into three main types: Internal variation, external variation, and unit to unit variation.
Internal variation is due to deterioration such as the wear of a machine and aging of materials. External variation is from factors relating to environmental conditions such as temperature, humidity and dust. Unit to Unit variation is variation between parts due to variations in material, processes and equipment.
Examples of robust design include: An engine mounting will not deteriorate when exposed to varying environments (external variation), food products that have long shelf lives (internal variation), and replacement parts that will fit properly (unit to unit variation). The goal of robust design is to come up with a way to make the final product consistent when the process is subject to a variety of ‘noise’.
How to make a design robust?
Taguchi considers making a design robust in the parameter design portion of product or process design. In parameter design, the goal is to find values for controllable settings that minimize the negative effects of the uncontrollable settings. Experiments are used to determine the impact of particular settings on both the controllable and uncontrollable factors. The idea here is that by observing changes in a controllable factor (such as viscosity of latex), a value can be found for that factor that reduces the effect (thickness ) of something that can’t be controlled (the humidity outside). The ultimate goal is to find the optimal settings to minimize cost by minimizing variation.
When setting up these experiments, the factors that affect the product need to be determined. Then the factors can be separated into controllable factors and uncontrollable factors, and experiments can be set up to test the effects of changing the values of each factor. There are many ways to set up these experiments.
Taguchi’s method involves finding correlation between variables. He uses orthogonal arrays, with the inner array consisting of control factors and the outer array consisting of “noise” factors. Each inner array is to be run with each outer array. (If six control factor experiments and three “noise” factor experiments are needed, there will have to be (six times three) eighteen experimental trials to get all the combinations).
Another method for conducting these experiments is to make no attempt to control the “noise” factors, but repeatedly run the trials for combinations of control factors. This type of experiment allows the operator to measure process variability. The trials should be taken in an environment similar to the one in which the actual use or manufacturing of the product is going to take place.
A third experimental design is to identify all the control and ‘noise’ factors (adding the control and noise factors yields k) and run an analysis using at least k +1 trials based on eight-run experiments. (You could use an eight-run experiment for up to k=7, and a sixteen-run experiment for up to k=15.) This will allow the interaction between variable to be seen running fewer tests than using Taguchi’s method. (Further instruction as to how to use this method is found in chapter four of Designing for Quality by Lochner and Matar.)
The data found from the experimental trials is then analyzed. The analysis will depend on the method of experimentation. Plot the effect that the variables had on your variation and/or the correlation between factors. Using this data, find settings for the controllable factors that are found to lower the variation caused by uncontrollable factors.
Then, after the initial experiment trails are run and ‘optimal’ settings are found, the confirmation experimentation is needed. By performing a series of replica experiments at the levels that were picked, we can see if the values achieved matched the values of the model predicted. If there is disparity, there may be an interaction or noise that we didn’t see and thus our experiment must be redeveloped.
Advantages of robust design
Robust design has many advantages. For one, the effect of robustness on quality is great. Robustness reduces variation in parts by reducing the effects of uncontrollable variation. More consistent parts equals better quality.
Another advantage is that lower quality parts or parts with higher tolerances can be used, and a quality product can still be made. This saves the company money, because the less variable the parts can be the more they cost.
A third advantage is that the product will have more appeal to the customer. Customers demand a robust product that won’t be as vulnerable to deterioration and can be used in a variety of situations.
This method is also good, because you are designing the robustness into the product and process instead of trying to fix variation problem after they occur.
One of the disadvantages of robust design is that, to effectively deal with the noise, the designer must be aware of the noise. If there is a noise factor that is affecting the product and the experiments run do not address it (intentionally or not), the only way that the product will be robust to that variation is only by luck.
Another disadvantage to robust design done Taguchi’s way is that the problem becomes large quickly. If you had a lot of different things to consider as control variables and/or noise variables, it would take a great deal of time to run all the experimental trials. Controlling noise variables is expensive, and when lots of trials are required the dollars add up.
Simple example of a Robust design
Consider the example to maintain the latex Ph around 10.0 with different environment (temperature). In this example, the chemists try different materials which can maintain the Ph of latex at different temperatures, with out affecting the final quality of the product produced by using this latex.